With the advent of miniaturised sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free-living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.

Abstract-Driven by the demands on healthcare resulting from the shift toward more sedentary lifestyles, considerable effort has been devoted to the monitoring and classification of human activity. In previous studies, various classification schemes and feature extraction methods have been used to identify different activities from a range of different datasets. In this paper, we present a comparison of 14 methods to extract classification features from accelerometer signals. These are based on the wavelet transform and other well-known time-and frequency-domain signal characteristics. To allow an objective comparison between the different features, we used two datasets of activities collected from 20 subjects. The first set comprised three commonly used activities, namely, level walking, stair ascent, and stair descent, and the second a total of eight activities. Furthermore, we compared the classification accuracy for each feature set across different combinations of three different accelerometer placements. The classification analysis has been performed with robust subject-based cross-validation methods using a nearest-neighbor classifier. The findings show that, although the wavelet transform approach can be used to characterize nonstationary signals, it does not perform as accurately as frequencybased features when classifying dynamic activities performed by healthy subjects. Overall, the best feature sets achieved over 95% intersubject classification accuracy.

In the present study we have estimated the temporal elongation of the plantar aponeurosis (PA) during normal walking using a subject-specific multi-segment rigid-body model of the foot. As previous studies have suggested that muscular forces at the ankle can pre-load the PA prior to heel-strike, the main purpose of the current study was to test, through modelling, whether there is any tension present in the PA during early stance phase. Reflective markers were attached to bony landmarks to track the kinematics of the calcaneus, metatarsus and toes during barefoot walking. Ultrasonography measurements were performed on three subjects to determine both the location of the origin of the PA on the plantar aspect of the calcaneus, and the radii of the metatarsal heads. Starting with the foot in a neutral, unloaded position, inverse kinematics allowed calculation of the tension in the five slips of the PA during the whole duration of the stance phase. The results show that the PA experienced tension significantly above rest during early stance phase in all subjects (P<0.01), thus providing support for the PA-preloading hypothesis. The amount of preloading and the maximum elongation of the slips of the PA decreased from medial to lateral. The mean maximum tension exerted by the PA was 1.5 BW (body weight) over the three subjects.

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